def test_streaming_transducer():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = StreamingTransducer(vocabulary_size=text_featurizer.num_classes,
**config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer)
concrete_func = model.make_tflite_function(
timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(
timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
converter.convert()
print("Converted successfully with timestamp")
assert args.saved
if args.tfrecords:
test_dataset = ASRTFRecordTestDataset(
data_paths=config.learning_config.dataset_config.test_paths,
tfrecords_dir=config.learning_config.dataset_config.tfrecords_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="test",
shuffle=False)
else:
test_dataset = ASRSliceTestDataset(
data_paths=config.learning_config.dataset_config.test_paths,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="test",
shuffle=False)
# build model
streaming_transducer = StreamingTransducer(
vocabulary_size=text_featurizer.num_classes, **config.model_config)
streaming_transducer._build(speech_featurizer.shape)
streaming_transducer.load_weights(args.saved, by_name=True)
streaming_transducer.summary(line_length=150)
streaming_transducer.add_featurizers(speech_featurizer, text_featurizer)
streaming_transducer_tester = BaseTester(
config=config.learning_config.running_config, output_name=args.output_name)
streaming_transducer_tester.compile(streaming_transducer)
streaming_transducer_tester.run(test_dataset)
help="Path to saved model")
parser.add_argument("output", type=str, default=None,
help="TFLite file path to be exported")
args = parser.parse_args()
assert args.saved and args.output
config = UserConfig(DEFAULT_YAML, args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config["speech_config"])
text_featurizer = CharFeaturizer(config["decoder_config"])
# build model
streaming_transducer = StreamingTransducer(
**config["model_config"],
vocabulary_size=text_featurizer.num_classes
)
streaming_transducer._build(speech_featurizer.shape)
streaming_transducer.load_weights(args.saved)
streaming_transducer.summary(line_length=150)
streaming_transducer.add_featurizers(speech_featurizer, text_featurizer)
concrete_func = streaming_transducer.make_tflite_function(greedy=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
if not os.path.exists(os.path.dirname(args.output)):
os.makedirs(os.path.dirname(args.output))
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
**vars(config.learning_config.train_dataset_config))
eval_dataset = ASRSliceDataset(
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
**vars(config.learning_config.eval_dataset_config))
streaming_transducer_trainer = TransducerTrainer(
config=config.learning_config.running_config,
text_featurizer=text_featurizer,
strategy=strategy)
with streaming_transducer_trainer.strategy.scope():
# build model
streaming_transducer = StreamingTransducer(
**config.model_config, vocabulary_size=text_featurizer.num_classes)
streaming_transducer._build(speech_featurizer.shape)
streaming_transducer.summary(line_length=150)
optimizer = tf.keras.optimizers.get(
config.learning_config.optimizer_config)
streaming_transducer_trainer.compile(model=streaming_transducer,
optimizer=optimizer,
max_to_keep=args.max_ckpts)
streaming_transducer_trainer.fit(train_dataset,
eval_dataset,
train_bs=args.tbs,
eval_bs=args.ebs)
})
speech_featurizer = TFSpeechFeaturizer({
"sample_rate": 16000,
"frame_ms": 25,
"stride_ms": 10,
"num_feature_bins": 80,
"feature_type": "log_mel_spectrogram",
"preemphasis": 0.97,
"normalize_signal": True,
"normalize_feature": True,
"normalize_per_feature": False
})
model = StreamingTransducer(vocabulary_size=text_featurizer.num_classes,
encoder_dmodel=320,
encoder_nlayers=3)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.save_weights("/tmp/transducer.h5")
model.add_featurizers(speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer)
features = tf.zeros(shape=[5, 50, 80, 1], dtype=tf.float32)
pred = model.recognize(features)
print(pred)
pred = model.recognize_beam(features)
print(pred)
def test_streaming_transducer():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = StreamingTransducer(vocabulary_size=text_featurizer.num_classes, **config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(speech_featurizer=speech_featurizer, text_featurizer=text_featurizer)
concrete_func = model.make_tflite_function(timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.convert()
print("Converted successfully with timestamp")
tflitemodel = tf.lite.Interpreter(model_content=tflite_model)
signal = tf.random.normal([4000])
input_details = tflitemodel.get_input_details()
output_details = tflitemodel.get_output_details()
tflitemodel.resize_tensor_input(input_details[0]["index"], signal.shape)
tflitemodel.allocate_tensors()
tflitemodel.set_tensor(input_details[0]["index"], signal)
tflitemodel.set_tensor(
input_details[1]["index"],
tf.constant(text_featurizer.blank, dtype=tf.int32)
)
tflitemodel.set_tensor(
input_details[2]["index"],
tf.zeros(
[config.model_config["encoder_nlayers"], 2, 1, config.model_config["encoder_rnn_units"]],
dtype=tf.float32
)
)
tflitemodel.set_tensor(
input_details[3]["index"],
tf.zeros(
[config.model_config["prediction_num_rnns"], 2, 1, config.model_config["prediction_rnn_units"]],
dtype=tf.float32
)
)
tflitemodel.invoke()
hyp = tflitemodel.get_tensor(output_details[0]["index"])
print(hyp)